The ability to collect and analyze data to determine who is driving a vehicle has many valuable applications, for example, relating to vehicle and driver insurance, vehicle financing, product safety and marketing, government and law enforcement, and various other applications in other industries. The goal of driver detection, or driver fingerprinting, is to determine whether a user recording a car trip with a computing device is a driver or a passenger of the vehicle. If driver profiles are known or have been determined for all potential drivers of a vehicle, then the solution becomes one of driver identification. If all potential drivers are known the solution becomes one of a forced task choice that determines which driver profile is the closest match in the database.
In contrast, solving the problem of driver authentication involves determining the driver from a pool of drivers that may be largely unknown. Solving such a problem is needed and would have many valuable applications. Further complications that need to be overcome in the context of driver authentication include making such determinations based on unsupervised, i.e. unlabeled data. Additionally, there is a need to determine driver authentication based on a method which is agnostic to road, traffic, and weather conditions. Finally, a need exists for a method and system to determine driver authentication based on collected real-time data. Such real-time data may be collected in non-uniform/varying road, traffic, and weather conditions.